Generally, “insurance” is an agreement by which an insurer, sometimes referred to as an underwriter, undertakes to indemnify the insured party against loss, damage, or liability arising from certain risks, in exchange for consideration. The consideration paid by an insured party is typically referred to as a “premium,” which is paid to keep the insurance in effect. An “insurance policy” is a contract of insurance that defines the rights and duties of the contracting parties. A typical insurance policy includes limits on the amount of risk that the insurer will cover.
For the purposes of the following discussion, an “insurance product” includes more than the insurance policy. It also includes services, distribution channels, and other components that may impact the customer experience.
Property insurance protects persons or businesses from financial loss caused by perils. Perils can include losses due to fire, water, earthquake, wind, explosions, aircraft damage (as when an aircraft crashes into a structure), lightning, hail, riot or civil commotion, smoke, vandalism, falling objects, theft, volcanic eruptions, and freezing. An insurance policy providing property insurance may cover some or all of these categories of perils. By paying a premium on a regular basis, a policyholder is insured against a loss caused by a peril within the scope of the policy.
Insurance rates are determined through an actuarial process. The process looks at data related to customer characteristics to determine differences in expected loss costs for different customers. One part of the actuarial process, referred to as “territorial ratemaking,” is an actuarial process for adjusting rates in insurance or other risk transfer mechanisms based on location. The ratemaking process is prospective because property and casualty rates are typically developed prior to the transfer of risk from a customer to an insurance company. Accordingly, the rate in an insurance policy reflects an estimate of the expected value of future costs, so techniques estimate future losses and costs to determine insurance rates.
By analyzing loss-cost data of a region over a number of years, a company can estimate future exposure to risk more accurately by invoking mathematical methodologies. In the insurance industry, a common practice for determining rates involves estimating future costs by looking at past loss-cost data. Different actuarial methodologies further improve the credibility of the data available in the ratemaking process.
Data in many companies and government agencies is often organized according to ZIP code. Often, there is a need to identify geographical relationships of occurrences or experiences of a particular group with the experiences of another group. However, experiences may be less relevant if the other group is located a significant distance from the studied group's location.
The credibility of the data is a predictive value that an actuary attaches to a particular body of data. One way of increasing the credibility of the data is to increase the size of the group or to make groupings more homogeneous. Loss cost data is frequently broken down into commonly used, geographically defined regions such as postal ZIP codes. When data is sorted by ZIP code, some areas defined by ZIP codes may have little or no recent data from which to predict premiums accurately.
Pure premium data values can vary significantly among ZIP codes. The same data can also change significantly from year to year. In addition, losses from a given peril are statistically just as likely to occur across the street or on the next block, as where a particular loss occurred. The property located on the other side of the street may fall, however, within a different ZIP code where few if any such losses were experienced.
It is reasonable to assume that properties in neighboring areas are most likely to experience the same loss as the property that experienced the actual loss. However, an exposure to all perils may have a measurable difference in frequency or severity for properties that get further away from a given location.
One way to estimate bordering ZIP codes is to determine the shortest distance of the target ZIP code to the closest centroid of a neighboring ZIP code. However, using such a method creates a problem when an automated system cannot determine whether the shortest distance describes the only neighboring ZIP code or if there are others, and if so, how many there are. Given an irregular border, many ZIP codes could border the target ZIP code. In addition, because a centroid of a region may not be located in the center or even within a region, using such a methodology based on centroids might not be accurate. As a result, centroids alone may not be the best approach for determining neighboring ZIP codes.
Therefore, it is useful to relate the experience of neighboring properties and recognize that the relevance of the neighboring experience will diminish with distance.